Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review

• URL: http://arxiv.org/abs/2404.11597v2
• Date: Mon, 10 Jun 2024 17:04:10 GMT
• Title: Explainable Artificial Intelligence Techniques for Accurate Fault Detection and Diagnosis: A Review
• Authors: Ahmed Maged, Salah Haridy, Herman Shen,
• Abstract summary: We review the eXplainable AI (XAI) tools and techniques in this context. We focus on their role in making AI decision-making transparent, particularly in critical scenarios where humans are involved. We discuss current limitations and potential future research that aims to balance explainability with model performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the manufacturing industry advances with sensor integration and automation, the opaque nature of deep learning models in machine learning poses a significant challenge for fault detection and diagnosis. And despite the related predictive insights Artificial Intelligence (AI) can deliver, advanced machine learning engines often remain a black box. This paper reviews the eXplainable AI (XAI) tools and techniques in this context. We explore various XAI methodologies, focusing on their role in making AI decision-making transparent, particularly in critical scenarios where humans are involved. We also discuss current limitations and potential future research that aims to balance explainability with model performance while improving trustworthiness in the context of AI applications for critical industrial use cases.

Related papers

• Adaptation of XAI to Auto-tuning for Numerical Libraries [0.0]
  Explainable AI (XAI) technology is gaining prominence, aiming to streamline AI model development and alleviate the burden of explaining AI outputs to users. This research focuses on XAI for AI models when integrated into two different processes for practical numerical computations.
  arXiv  Detail & Related papers  (2024-05-12T09:00:56Z)
• Towards a general framework for improving the performance of classifiers using XAI methods [0.0]
  This paper proposes a framework for automatically improving the performance of pre-trained Deep Learning (DL) classifiers using XAI methods. We will call auto-encoder-based and encoder-decoder-based, and discuss their key aspects.
  arXiv  Detail & Related papers  (2024-03-15T15:04:20Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• AI Maintenance: A Robustness Perspective [91.28724422822003]
  We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance. We propose an AI model inspection framework to detect and mitigate robustness risks. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
  arXiv  Detail & Related papers  (2023-01-08T15:02:38Z)
• A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection [0.0]
  Trust in technology is enabled by understanding the rationale behind the predictions made. For cross-sectional data classical XAI approaches can lead to valuable insights about the models' inner workings. We propose a novel XAI technique for deep learning methods which preserves and exploits the natural time ordering of the data.
  arXiv  Detail & Related papers  (2022-12-06T12:04:01Z)
• Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis [71.2468615993246]
  Building intelligent transportation systems requires an intricate combination of artificial intelligence and mobility analysis. The past few years have seen rapid development in transportation applications using advanced deep neural networks. This vision paper emphasizes research challenges in deep learning-based mobility analysis that require interpretability and robustness.
  arXiv  Detail & Related papers  (2022-10-18T17:28:58Z)
• Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey [1.7205106391379026]
  Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are critical, such black-box AI models are not sufficient. Explainable Artificial Intelligence (XAI) addresses this problem and defines a set of AI models that are interpretable by the users.
  arXiv  Detail & Related papers  (2022-06-07T08:22:30Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• A Review of Explainable Artificial Intelligence in Manufacturing [0.8793721044482613]
  The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations. Despite the high accuracy of these models, they are mostly considered black boxes: they are unintelligible to the human. We present an overview of Explainable Artificial Intelligence (XAI) techniques as a means of boosting the transparency of models.
  arXiv  Detail & Related papers  (2021-07-05T21:59:55Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.